Lviv Data Science Summer School Online
Lviv Data Science Summer School is an educational initiative of the Faculty of Applied Sciences, Ukrainian Catholic University. The summer school participants – undergraduates, Ph.D. students, young professionals – study state-of-the-art methods and tools of Data Science and Machine Learning. The school is oriented towards the intermediate level of participants’ skills and knowledge.
The dates of the school in 2020 are July 20th – 31st.
Important! Due to the COVID-19 pandemic, the Lviv Data Science Summer School is transferred to an online format. We make some changes like more concentration on the courses, skipping the project work (unfortunately). The new online format allows having many more participants thus we canceled the preliminary selection of the applicants and prolonged the registration until July 1st. This a notification from April 30.
The goal of the summer school is to give the practice-oriented knowledge in the field of Data Science.
The students join the project’s work. The project topics are provided by the supervisors: school’s lecturers, representatives of IT companies, and other partner organizations. The project work during several days means implementation and approbation of the previously obtained knowledge and skills. The project teams present their results publicly at the end of the school. The participants can gain 3 ECTS credits after the school completion. Please pay attention that each school’s participant will take only four courses from the list below. The courses go in parallel. During the schedule formation, the organizers take into account the requests from the participants about their preferred courses.
The participants may take any of the listed courses in various domains of Data Science and Machine Learning. The list is keeping updated.
- Game Theory and Applications
- Machine Learning for Financial Data Structures
- Machine Teaching
- Multi-agent Reinforcement Learning
- Machine Learning in Healthcare
- Deep Learning for Audio
- Introduction to Probabilistic Programming for Scientific Discovery
- Graph Neural Networks – harvesting relations
- Fundamentals of Causal Learning
- Fairness in Machine Learning
The scientists from well-known universities and the leading specialists of the Ukrainian and world’s companies teach at the Summer School.
The participants should be familiar with the basics of Statistical Inference, Machine Learning, Python programming (also R programming will be used at some courses). Detailed knowledge prerequisites together with the recommendations for the individual preparation can be found at the “Terms of service” web page under the Prerequisites section.
Participation in the Summer School is paid. The price does not include accommodation and meals. Ukrainian students – 7 000 UAH Ukrainian participants, except full-time students – 13 500 UAH Non-Ukrainian participants – 500 EUR In case one needs financial support, the organizers might consider the request for a discount of up to 30%. More information on the Discounts web page.
The participation at the online school is free of charge prior to the registration. The registration form link: zfrmz.com/i7M1SBzwT6HSXenli5fw
The registration deadline is July 1st.
Support the school
While the participation at the school is free for everyone, we kindly ask you to support us if you may and donate 10, 20, or 30 EUR. The collected funds will be spent on school’s technical spendings and scholarship for the talented students at our Data Science master program.
If one wants to participate at the school but does not have sufficient knowledge we propose a preparatory event – Warm-up Week. During the week before the main school begins, you will get into the basics of Statistical Interference, Machine Learning, and Data Visualization.
The warm-up week is canceled.
Previous Summer Schools
- Lviv Data Science Summer School 2016
- Lviv Data Science Summer School 2017
- Lviv Data Science Summer School 2018
- Lviv Data Science Summer School 2019
In case you have any questions, please contact us via
Email: [email protected]